



from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
	import datasets







class 				_a				(   _lowerCAmelCase					):

			@slow
			@require_torch
			def      __snake_case       (self					)       ->   List[Any]:
						UpperCAmelCase_:		Optional[Any]							      =     EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""",   """prajjwal1/bert-tiny"""					)
						UpperCAmelCase_:		Optional[Any]							      =     BertTokenizer.from_pretrained("""bert-base-uncased"""					)

						UpperCAmelCase_:		Tuple							      =     bertabert.config.encoder.vocab_size
						UpperCAmelCase_:		Dict							      =     tokenizer.sep_token_id
						UpperCAmelCase_:		Any							      =     tokenizer.cls_token_id
						UpperCAmelCase_:		Tuple							      =     128

						UpperCAmelCase_:		Optional[Any]							      =     datasets.load_dataset("""cnn_dailymail""",   """3.0.0""",   split="""train[:1%]"""					)
						UpperCAmelCase_:		Any							      =     datasets.load_dataset("""cnn_dailymail""",   """3.0.0""",   split="""validation[:1%]"""					)

						UpperCAmelCase_:		str							      =     train_dataset.select(range(32					)					)
						UpperCAmelCase_:		Optional[Any]							      =     val_dataset.select(range(16					)					)

						UpperCAmelCase_:		str							      =     4

						def _map_to_encoder_decoder_inputs(SCREAMING_SNAKE_CASE_					):
									# Tokenizer will automatically set [BOS] <text> [EOS]
									UpperCAmelCase_:		Any							      =     tokenizer(batch["""article"""],   padding="""max_length""",   truncation=SCREAMING_SNAKE_CASE_,   max_length=512					)
									UpperCAmelCase_:		Optional[Any]							      =     tokenizer(batch["""highlights"""],   padding="""max_length""",   truncation=SCREAMING_SNAKE_CASE_,   max_length=128					)
									UpperCAmelCase_:		Any							      =     inputs.input_ids
									UpperCAmelCase_:		Union[str, Any]							      =     inputs.attention_mask

									UpperCAmelCase_:		Optional[int]							      =     outputs.input_ids
									UpperCAmelCase_:		Optional[Any]							      =     outputs.input_ids.copy()
									UpperCAmelCase_:		Tuple							      =     [
									    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
									]
									UpperCAmelCase_:		int							      =     outputs.attention_mask

									assert all(len(SCREAMING_SNAKE_CASE_					) == 512 for x in inputs.input_ids					)
									assert all(len(SCREAMING_SNAKE_CASE_					) == 128 for x in outputs.input_ids					)

									return batch

						def _compute_metrics(SCREAMING_SNAKE_CASE_					):
									UpperCAmelCase_:		Tuple							      =     pred.label_ids
									UpperCAmelCase_:		Any							      =     pred.predictions

									# all unnecessary tokens are removed
									UpperCAmelCase_:		Optional[Any]							      =     tokenizer.batch_decode(SCREAMING_SNAKE_CASE_,   skip_special_tokens=SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		Union[str, Any]							      =     tokenizer.batch_decode(SCREAMING_SNAKE_CASE_,   skip_special_tokens=SCREAMING_SNAKE_CASE_					)

									UpperCAmelCase_:		str							      =     sum([int(pred_str[i] == label_str[i]					) for i in range(len(SCREAMING_SNAKE_CASE_					)					)]					) / len(SCREAMING_SNAKE_CASE_					)

									return {"accuracy": accuracy}

						# map train dataset
						UpperCAmelCase_:		int							      =     train_dataset.map(
						    _map_to_encoder_decoder_inputs,   batched=SCREAMING_SNAKE_CASE_,   batch_size=SCREAMING_SNAKE_CASE_,   remove_columns=["""article""", """highlights"""],   )
						train_dataset.set_format(
						    type="""torch""",   columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""],   )

						# same for validation dataset
						UpperCAmelCase_:		Optional[int]							      =     val_dataset.map(
						    _map_to_encoder_decoder_inputs,   batched=SCREAMING_SNAKE_CASE_,   batch_size=SCREAMING_SNAKE_CASE_,   remove_columns=["""article""", """highlights"""],   )
						val_dataset.set_format(
						    type="""torch""",   columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""],   )

						UpperCAmelCase_:		str							      =     self.get_auto_remove_tmp_dir()

						UpperCAmelCase_:		Optional[Any]							      =     SeqaSeqTrainingArguments(
						    output_dir=SCREAMING_SNAKE_CASE_,   per_device_train_batch_size=SCREAMING_SNAKE_CASE_,   per_device_eval_batch_size=SCREAMING_SNAKE_CASE_,   predict_with_generate=SCREAMING_SNAKE_CASE_,   evaluation_strategy="""steps""",   do_train=SCREAMING_SNAKE_CASE_,   do_eval=SCREAMING_SNAKE_CASE_,   warmup_steps=0,   eval_steps=2,   logging_steps=2,   )

						# instantiate trainer
						UpperCAmelCase_:		Union[str, Any]							      =     SeqaSeqTrainer(
						    model=SCREAMING_SNAKE_CASE_,   args=SCREAMING_SNAKE_CASE_,   compute_metrics=_compute_metrics,   train_dataset=SCREAMING_SNAKE_CASE_,   eval_dataset=SCREAMING_SNAKE_CASE_,   tokenizer=SCREAMING_SNAKE_CASE_,   )

						# start training
						trainer.train()











from ...configuration_utils import PretrainedConfig
from ...utils import logging


a      :  List[Any]			     =			logging.get_logger(__name__)







class 				_a				(   _lowerCAmelCase					):
			A      		=	'''timm_backbone'''

			def __init__(self,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=None,   **SCREAMING_SNAKE_CASE_,   )       ->   Tuple:
						super().__init__(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Dict							      =     backbone
						UpperCAmelCase_:		Optional[Any]							      =     num_channels
						UpperCAmelCase_:		Optional[Any]							      =     features_only
						UpperCAmelCase_:		Any							      =     use_pretrained_backbone
						UpperCAmelCase_:		List[str]							      =     True
						UpperCAmelCase_:		Union[str, Any]							      =     out_indices if out_indices is not None else (-1,)






